Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
In [1]:
import numpy as np
import cv2
import glob
import pickle
import matplotlib.pyplot as plt
from moviepy.editor import VideoFileClip
from IPython.display import HTML
%matplotlib inline
In [2]:
def show_couple(img1, img2, title1=None, title2=None):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
if len(img1.shape) <= 2:
ax1.imshow(img1, cmap='gray')
else:
ax1.imshow(img1)
if title1:
ax1.set_title(title1, fontsize=50)
if len(img2.shape) <= 2:
ax2.imshow(img2, cmap='gray')
else:
ax2.imshow(img2)
if title2:
ax2.set_title(title2, fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
Calibrate Camera¶
In [3]:
def call_undistort(img, objpoints, imgpoints):
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
In [4]:
nx = 9 # the number of insicall_undistortde corners in x
ny = 6 # the number of inside corners in y
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny*nx,3), np.float32)
objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('./camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx,ny), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
undist = call_undistort(img, objpoints, imgpoints)
undist2 = np.copy(undist)
# Draw and display the corners
drawn = cv2.drawChessboardCorners(undist, (nx,ny), corners, ret)
total = nx * ny
src = np.float32([corners[0][0], corners[nx-1][0], corners[total - nx][0], corners[total -1][0]])
box_h = corners[nx][0][1] - corners[0][0][1]
d_sh = img.shape[1::-1]
dst = np.float32([[box_h,box_h],[d_sh[0] - box_h,box_h],[box_h,d_sh[1] - box_h],[d_sh[0] - box_h,d_sh[1] - box_h]])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(undist2, M, img.shape[1::-1], flags=cv2.INTER_LINEAR)
show_couple(drawn, warped, 'Original Image', 'Undistorted Image')
Compute Calibration¶
In [5]:
img = cv2.imread('./camera_cal/calibration1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
undist = cv2.undistort(img, mtx, dist, None, mtx)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
calibration = {"mtx" : mtx, "dist": dist}
pickle.dump(calibration, open( "calibration.p", "wb" ) )
# Visualize undistortion
show_couple(img, undist, 'Original Image', 'Undistorted Image')
Test Images¶
Undistort images¶
In [6]:
def undistort(img):
return cv2.undistort(img, mtx, dist, None, mtx)
In [7]:
test = {}
test['img'] = cv2.imread('./test_images/straight_lines1.jpg')
test['img'] = cv2.cvtColor(test['img'],cv2.COLOR_BGR2RGB)
test['dst'] = undistort(test['img'])
show_couple(test['img'], test['dst'], 'Original Image', 'Undistorted Image')
Perspective Transform¶
In [8]:
def unwarp(img, src, dst):
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
warped = cv2.warpPerspective(img, M, img.shape[1::-1], flags=cv2.INTER_LINEAR)
return warped, M, Minv
In [9]:
shp = test['dst'].shape[1::-1]
src = np.float32([
[580, 450],
[shp[0] - 500, 450],
[shp[0] - 90, shp[1]-55],
[150, shp[1]-55],
])
dst = np.float32([[0,0],[shp[0],0],[shp[0], shp[1]],[0,shp[1]]])
In [10]:
test['wrp'], test['M'], test['Mi'] = unwarp(test['dst'], src, dst)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(test['dst'])
for i in range(4):
j = (i + 1) % 4
ax1.plot((src[i][0], src[j][0]), (src[i][1], src[j][1]), color="red")
ax1.set_title('Undistorted Image', fontsize=50)
ax2.imshow(test['wrp'])
ax2.set_title('Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
Sobel Absolute Threshold¶
In [11]:
# Define a function that applies Sobel x or y,
# then takes an absolute value and applies a threshold.
def abs_sobel_thresh(img, orient='x', thresh_min=25, thresh_max=255):
# Apply the following steps to img
# 1) Convert to grayscale === or LAB L channel
gray = (cv2.cvtColor(img, cv2.COLOR_RGB2Lab))[:,:,0]
# 2) Take the derivative in x or y given orient = 'x' or 'y'
sobel = cv2.Sobel(gray, cv2.CV_64F, orient=='x', orient=='y')
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# 6) Return this mask as your binary_output image
return sxbinary
In [12]:
test['sbl'] = abs_sobel_thresh(test['wrp'], 'x', 25, 255)
# Visualize sobel absolute threshold
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(test['wrp'])
ax1.set_title('Unwarped Image', fontsize=30)
ax2.imshow(test['sbl'], cmap='gray')
ax2.set_title('Sobel Absolute', fontsize=30)
Out[12]:
Sobel Magnitude Threshold¶
In [13]:
# Define a function that applies Sobel x and y,
# then computes the magnitude of the gradient
# and applies a threshold
def mag_thresh(img, kernel=25, thresh=(25, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, kernel)
# 3) Calculate the magnitude
mag_sobel = np.sqrt(np.square(sobelx) + np.square(sobely))
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobel = np.uint8(255*mag_sobel/np.max(mag_sobel))
# 5) Create a binary mask where mag thresholds are met
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return sxbinary
In [14]:
test['mag'] = mag_thresh(test['wrp'], kernel=31, thresh=(10, 255))
# Visualize sobel magnitude threshold
show_couple(test['wrp'], test['mag'], 'Unwarped Image', 'Sobel Magnitude')
Sobel Direction Threshold¶
In [15]:
# Define a function that applies Sobel x and y,
# then computes the direction of the gradient
# and applies a threshold.
def dir_thresh(img, kernel=7, thresh=(0, 0.09)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
grad_dir = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(grad_dir)
binary_output[(grad_dir >= thresh[0]) & (grad_dir <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
In [16]:
test['dir'] = dir_thresh(test['wrp'], kernel=7, thresh=(0, 0.09))
# Visualize sobel direction threshold
show_couple(test['wrp'], test['dir'], 'Unwarped Image', 'Sobel Direction')
Sobel Magnitude & Direction Threshold¶
In [17]:
test['m+d'] = np.zeros_like(test['mag'])
test['m+d'][((test['mag'] == 1) & (test['dir'] == 1))] = 1
# Visualize sobel magnitude + direction threshold
show_couple(test['wrp'], test['m+d'], 'Unwarped Image', 'Sobel Magnitude + Direction')
HLS Colorspace S-Channel Threshold¶
In [18]:
# Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_s_thresh(img, thresh=(125, 255)):
# 1) Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
# 2) Apply a threshold to the S channel
binary_output = np.zeros_like(hls[:,:,2])
binary_output[(hls[:,:,2] > thresh[0]) & (hls[:,:,2] <= thresh[1])] = 1
# 3) Return a binary image of threshold result
return binary_output
In [19]:
test['hlS'] = hls_s_thresh(test['wrp'], (40, 255))
show_couple(test['wrp'], test['hlS'], "Unwarped Image", "HLS S-Channel")
HLS Colorspace L-Channel¶
In [20]:
# Define a function that thresholds the L-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_l_thresh(img, thresh=(220, 255)):
# 1) Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
hls_l = hls[:,:,1]
hls_l = hls_l*(255/np.max(hls_l))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(hls_l)
binary_output[(hls_l > thresh[0]) & (hls_l <= thresh[1])] = 1
# 3) Return a binary image of threshold result
return binary_output
In [21]:
test['hLs'] = hls_l_thresh(test['wrp'], (220, 255))
show_couple(test['wrp'], test['hLs'], "Unwarped Image", "HLS L-Channel")
LAB Colorspace B-Channel¶
In [22]:
# Define a function that thresholds the B-channel of LAB
# Use exclusive lower bound (>) and inclusive upper (<=), OR the results of the thresholds (B channel should capture
# yellows)
def lab_b_thresh(img, thresh=(190, 255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_RGB2Lab)
lab_b = lab[:,:,2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 175:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 1
# 3) Return a binary image of threshold result
return binary_output
In [23]:
test['laB'] = lab_b_thresh(test['wrp'], (190, 255))
show_couple(test['wrp'], test['laB'], "Unwarped Image", "LAB B-Channel")
HLS L-Channel + LAB B-Channel¶
In [24]:
test['l+b'] = np.zeros_like(test['laB'])
test['l+b'][(test['hLs'] == 1) | (test['laB'] == 1)] = 1
show_couple(test['wrp'], test['l+b'], "Unwarped Image", "B+L-Channel")
HLS L-Channel + LAB B-Channel + Sobel Magnitude¶
In [109]:
test['l+b+m'] = np.zeros_like(test['laB'])
test['l+b+m'][(test['hLs'] == 1) | (test['laB'] == 1) | (test['mag'] == 1)] = 1
show_couple(test['wrp'], test['l+b+m'], "Unwarped Image", "B+L-Channel+Sobel")
Pipeline¶
In [116]:
def pipeline(img):
img_dst = undistort(img)
img_wrp, img_M, img_Minv = unwarp(img_dst, src, dst)
img_hLs = hls_l_thresh(img_wrp)
img_laB = lab_b_thresh(img_wrp)
img_mag = mag_thresh(img_wrp, kernel=31, thresh=(30, 255))
img_abs = abs_sobel_thresh(img_wrp, 'x', 25, 255)
img_bin = np.zeros_like(img_laB)
img_bin[(img_hLs == 1) | (img_laB == 1) | (img_abs == 1)] = 1
return img_bin, img_Minv
Run pipeline on test images¶
In [117]:
image_paths = glob.glob('./test_images/*.jpg')
images = []
for img_path in image_paths:
image = {}
image['img'] = cv2.imread(img_path)
image['img'] = cv2.cvtColor(image['img'], cv2.COLOR_BGR2RGB)
image['bin'], image['Minv'] = pipeline(image['img'])
show_couple(image['img'], image['bin'])
images.append(image)
Sliding Window¶
In [39]:
def sliding_window(binary_warped):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
quarter_point = np.int(midpoint//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
#leftx_base = np.argmax(histogram[quarter_point:midpoint]) + quarter_point
#rightx_base = np.argmax(histogram[midpoint:(midpoint+quarter_point)]) + midpoint
# Choose the number of sliding windows
nwindows = 10
# Set height of windows
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 120
# Set minimum number of pixels found to recenter window
minpix = 20
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high), (0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high), (0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
# Generate x and y values for plotting
left_fit, right_fit = None, None
if len(leftx) != 0:
left_fit = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit = np.polyfit(righty, rightx, 2)
return (left_lane_inds, right_lane_inds), (left_fit, right_fit), out_img
In [118]:
for image in images:
image['lin'], image['fit'], image['wnd'] = sliding_window(image['bin'])
show_couple(image['img'], image['wnd'], "Original Image", "Windows Found")
Visualize lines drawn¶
In [41]:
def visualize(image_bin, lin, fit):
margin = 80
nonzero = image_bin.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((image_bin, image_bin, image_bin)) * 255
window_img = np.zeros_like(out_img)
ploty = np.linspace(0, image_bin.shape[0]-1, image_bin.shape[0])
# Color in left and right line pixels
out_img[nonzeroy[lin[0]], nonzerox[lin[0]]] = [255, 0, 0]
out_img[nonzeroy[lin[1]], nonzerox[lin[1]]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_fit = fit[0]
right_fit = fit[1]
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0), lineType=cv2.LINE_AA)
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0), lineType=cv2.LINE_AA)
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.show()
In [119]:
for image in images:
visualize(image['bin'], image['lin'], image['fit'])
PolyFit with prior knowledge¶
In [43]:
def polyfit(binary_warped, left_fit_prev, right_fit_prev):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit_prev[0]*(nonzeroy**2) + left_fit_prev[1]*nonzeroy +
left_fit_prev[2] - margin)) & (nonzerox < (left_fit_prev[0]*(nonzeroy**2) +
left_fit_prev[1]*nonzeroy + left_fit_prev[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit_prev[0]*(nonzeroy**2) + right_fit_prev[1]*nonzeroy +
right_fit_prev[2] - margin)) & (nonzerox < (right_fit_prev[0]*(nonzeroy**2) +
right_fit_prev[1]*nonzeroy + right_fit_prev[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit, right_fit = None, None
if len(leftx) != 0:
left_fit = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit = np.polyfit(righty, rightx, 2)
return (left_lane_inds, right_lane_inds), (left_fit, right_fit)
Radius of Curvature and Distance from Lane Center Calculation¶
In [44]:
# Method to determine radius of curvature and distance from lane center
# based on binary image, polynomial fit, and L and R lane pixel indices
def curvature_distance(bin_img, fit_l, fit_r, lin_l, lin_r):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
rad_l, rad_r, center_dist = (0, 0, 0)
# Define y-value where we want radius of curvature
# I'll choose the maximum y-value, corresponding to the bottom of the image
h = bin_img.shape[0]
ploty = np.linspace(0, h-1, h)
y_eval = np.max(ploty)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = bin_img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Again, extract left and right line pixel positions
leftx = nonzerox[lin_l]
lefty = nonzeroy[lin_l]
rightx = nonzerox[lin_r]
righty = nonzeroy[lin_r]
if len(leftx) != 0:
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
rad_l = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
if len(rightx) != 0:
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
rad_r = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Distance from center is image x midpoint - mean of fit_l and fit_r intercepts
if fit_l is not None and fit_r is not None:
fit_l_x_int = fit_l[0]*h**2 + fit_l[1]*h + fit_l[2]
fit_r_x_int = fit_r[0]*h**2 + fit_r[1]*h + fit_r[2]
car_position = bin_img.shape[1]/2
lane_center_position = (fit_r_x_int + fit_l_x_int) /2
center_dist = (car_position - lane_center_position) * xm_per_pix
return (rad_l, rad_r), center_dist
In [45]:
for image in images:
image['rad'], image['cnt'] = curvature_distance(image['bin'], image['fit'][0], image['fit'][1], image['lin'][0], image['lin'][1])
print('Radius of curvature for example:', image['rad'][0], 'm,', image['rad'][1], 'm')
print('Distance from lane center for example:', image['cnt'], 'm')
Draw the Detected Lane Back onto the Original Image¶
In [99]:
def draw_lane(original_img, binary_img, Minv, left_fit, right_fit):
if left_fit is None or right_fit is None:
return original_img
new_img = np.copy(original_img)
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
ploty = np.linspace(0, binary_img.shape[0]-1, binary_img.shape[0])
h, w = binary_img.shape
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (184,225, 255))
cv2.polylines(color_warp, np.int32([pts_left]), isClosed=False, color=(255,0,255), thickness=15)
cv2.polylines(color_warp, np.int32([pts_right]), isClosed=False, color=(0,255,255), thickness=15)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (w, h))
# Combine the result with the original image
result = cv2.addWeighted(new_img, 1, newwarp, 0.5, 0)
return result
In [100]:
for image in images:
image['drn'] = draw_lane(image['img'], image['bin'], image['Minv'], image['fit'][0], image['fit'][1])
plt.imshow(image['drn'])
plt.show()
Draw Curvature Radius and Center Distance onto the Original Image¶
In [48]:
def draw_data(original_img, curv_rad, center_dist):
new_img = np.copy(original_img)
h = new_img.shape[0]
font = cv2.FONT_HERSHEY_DUPLEX
text = 'Curve radius: ' + '{:04.2f}'.format(curv_rad) + 'm'
cv2.putText(new_img, text, (40,70), font, 1.5, (200,255,155), 2, cv2.LINE_AA)
direction = ''
if center_dist > 0:
direction = 'right'
elif center_dist < 0:
direction = 'left'
abs_center_dist = abs(center_dist)
text = '{:04.3f}'.format(abs_center_dist) + 'm ' + direction + ' of center'
cv2.putText(new_img, text, (40,120), font, 1.5, (200,255,155), 2, cv2.LINE_AA)
return new_img
In [101]:
for image in images:
image['fnl'] = draw_data(image['drn'], (image['rad'][0] + image['rad'][1])/2, image['cnt'])
plt.imshow(image['fnl'])
plt.show()
Video Pipeline¶
Definitions¶
In [103]:
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = []
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
def sanity_check(self, R1):
if self.radius_of_curvature is None:
return True
R0 = self.radius_of_curvature
return abs(R1-R0)/R0 <= 0.85
def add_fit(self, fit, inds, rad):
if fit is not None:
if self.best_fit is not None:
# if we have a best fit, see how this new fit compares
self.diffs = abs(fit-self.best_fit)
self.detected = ((self.diffs[0] <= 0.001 and self.diffs[1] <= 1.0 and self.diffs[2] <= 100.)
or len(self.current_fit) == 0) and self.sanity_check(rad)
if self.detected:
self.radius_of_curvature = rad
self.current_fit.append(fit)
if len(self.current_fit) > 5:
# throw out old fits, keep newest n
self.current_fit = self.current_fit[1:]
self.best_fit = np.average(self.current_fit, axis=0)
else:
self.detected = False
if len(self.current_fit) > 0:
# throw out oldest fit
self.current_fit = self.current_fit[:len(self.current_fit)-1]
if len(self.current_fit) > 0:
# if there are still any fits in the queue, best_fit is their average
self.best_fit = np.average(self.current_fit, axis=0)
Pipeline¶
In [120]:
def process(img):
new_img = np.copy(img)
img_bin, Minv = pipeline(new_img)
if not l_line.detected or not r_line.detected:
lin, fit, _ = sliding_window(img_bin)
else:
lin, fit = polyfit(img_bin, l_line.best_fit, r_line.best_fit)
rad, d_center = curvature_distance(img_bin, fit[0], fit[1], lin[0], lin[1])
if abs(rad[0] - rad[1]) < 300:
l_line.add_fit(fit[0], lin[0], rad[0])
r_line.add_fit(fit[1], lin[1], rad[1])
# draw the current best fit if it exists
if l_line.best_fit is not None and r_line.best_fit is not None:
img_out1 = draw_lane(new_img, img_bin, Minv, l_line.best_fit, r_line.best_fit)
img_out = draw_data(img_out1, (rad[0]+rad[1])/2, d_center)
else:
img_out = new_img
return img_out
Process¶
In [121]:
l_line = Line()
r_line = Line()
video_out = 'project_video_output.mp4'
video_in = VideoFileClip('project_video.mp4')
processed_video = video_in.fl_image(process)
%time processed_video.write_videofile(video_out, audio=False)
In [123]:
l_line = Line()
r_line = Line()
video_out = 'challenge_video_output.mp4'
video_in = VideoFileClip('challenge_video.mp4')
processed_video = video_in.fl_image(process)
%time processed_video.write_videofile(video_out, audio=False)
In [71]:
l_line = Line()
r_line = Line()
video_out = 'harder_challenge_video_output.mp4'
video_in = VideoFileClip('harder_challenge_video.mp4')
processed_video = video_in.fl_image(process)
%time processed_video.write_videofile(video_out, audio=False)